RAG Documentation Search

Crawl4AI RAG

Enhance your knowledge access by leveraging a cutting-edge Model Context Protocol (MCP) that combines web crawling and RAG capabilities. This innovative approach allows for seamless retrieval and storage of website content in vector databases, paving the way for advanced semantic search functionalities across crawled data.

Ragie

Enhances connectivity with Ragie's knowledge repository system for streamlined retrieval and extraction of data from extensive datasets, optimizing search and information access.

Cloudflare Documentation

Enhance the linkage between AI platforms and Cloudflare's resources via Vectorize technology, facilitating advanced semantic search capabilities for accessing pertinent information on Cloudflare's offerings.

GraphRAG

Enhance your document search experience with a potent combination of Neo4j graph database and Qdrant vector database. Uncover semantic connections and expand structural context by seamlessly following relationships.

Minima (Local RAG)

Efficiently access and fetch contextual information from nearby documents for RAG applications.

RAG Docs

Enhances information retrieval through semantic search functionality and a vector database (Qdrant), facilitating streamlined access to extensive document repositories.

RAG Documentation

Experience advanced knowledge access with seamless integration of Qdrant vector search and documentation retrieval in Model Context Protocol (MCP). Unlock context-aware responses and enable semantic querying for a richer user experience.

Qdrant with OpenAI Embeddings

Unlock the potential of AI applications by seamlessly integrating them with Qdrant vector databases through the innovative Model Context Protocol (MCP). This cutting-edge protocol leverages OpenAI embeddings to empower semantic search capabilities, facilitating contextual document retrieval and enhancing knowledge base query processes.

ChromaDB

Enhance your natural language processing and information retrieval projects with seamless integration of advanced capabilities from ChromaDB vector database. Experience optimized semantic document search, storage, and retrieval functionalities for enhanced efficiency.

Code Context (Semantic Code Search)

Facilitates advanced code exploration and comprehension through the replication of git repositories, segmentation of code into meaningful sections, and creation of representations for simplified natural language search in extensive code repositories.

Documentation Search

Discover the latest content from well-known documentation platforms like LangChain, LlamaIndex, and OpenAI with seamless Google search integration and advanced content retrieval capabilities.

Journal RAG

Easily search and retrieve personal notes and reflections from your markdown journal using advanced vector database technology, enhancing the way you recall past memories, ideas, and events.

Rust Local RAG

Discover a cutting-edge Model Context Protocol (MCP) offering swift local document access and management. Leveraging Rust for unparalleled PDF handling capabilities and semantic search powered by Ollama embeddings, this protocol efficiently indexes PDF files within designated folders. Enjoy rapid document retrieval sans reliance on external solutions.

Pinecone Vector DB

Utilize Pinecone's vector databases to enhance semantic search capabilities and RAG functionality.

Rememberizer

Harness the capabilities of Rememberizer's document API for advanced semantic search and access to corporate intelligence powered by AI technology.